System Identification
Deep networks have set new standards for accurately predicting responses of neurons in visual cortex to arbitrary images. However, these models still do not account for all the explainable variability of the neuronal responses in primary visual cortex. We are building better models for the visual system of mice and monkeys that can accurately predict responses and their per trial variability as well as estimate the general state that the brain was in during presentation of arbitrary visual inputs.
Lab members are shown in this color.
2023
Konstantin-Klemens Lurz1, Mohammad Bashiri1, Edgar Y. Walker, Fabian H. Sinz
Bayesian Oracle for bounding information gain in neural encoding models
ICLR 2023 (accepted)
, equal contribution: 1
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2022
Erick Cobos, Taliah Muhammad, Paul G Fahey, Zhiwei Ding, Zhuokun Ding, Jacob Reimer, Fabian H. Sinz, Andreas Tolias
It takes neurons to understand neurons: Digital twins of visual cortex synthesize neural metamers
biorXiv
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Konstantin Lurz, Mohammad Bashiri, Fabian Sinz
Bayesian Oracle for bounding information gain in neural encoding models
Neurips 2022 Workshop InfoCog
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Luca Baroni1, Mohammad Bashiri1, Konstantin Friedrich Willeke, Ján Antolík, Fabian Sinz
Learning Invariance Manifolds of Visual Sensory Neurons
Neurips 2022 Workshop NeurReps
, equal contribution: 1
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Konstantin F. Willeke, Paul G. Fahey, Mohammad Bashiri, Laura Pede, Max F. Burg, Christoph Blessing, Santiago A. Cadena, Zhiwei Ding, Konstantin-Klemens Lurz, Kayla Ponder, Taliah Muhammad, Saumil S. Patel, Alexander S. Ecker, Andreas S. Tolias, Fabian H. Sinz
The Sensorium competition on predicting large-scale mouse primary visual cortex activity
arXiv
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2021
Mohammad Bashiri, Edgar Y. Walker, Konstantin-Klemens Lurz, Akshay Kumar Jagadish, Taliah Muhammad, Zhiwei Ding, Zhuokun Ding, Andreas S. Tolias, Fabian H. Sinz
A flow-based latent state generative model of neural population responses to natural images
NeurIPS (spotlight)
conference paper
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system identification
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openreview
twitter
teaser-youtube
talk-youtube
github
simulation demo
biorXiv
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Max F. Burg, Santiago A. Cadena, George H. Denfield, Edgar Y. Walker, Leon A. Gatys, Andreas S. Tolias, Matthias Bethge, Alexander S. Ecker
Learning Divisive Normalization in Primary Visual Cortex
PLoS Computational Biology
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Konstantin-Klemens Lurz, Mohammad Bashiri, Konstantin Friedrich Willeke, Akshay Kumar Jagadish, Eric Wang, Edgar Y Walker, Santiago Cadena, Taliah Muhammad, Eric Cobos, Andreas Tolias, Alexander Ecker, Fabian Sinz
Generalization in data-driven models of primary visual cortex
ICLR (spotlight)
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2020
James R. Cotton, Fabian H. Sinz, Andreas S. Tolias
Factorized Neural Processes for Neural Processes: K-Shot Prediction of Neural Responses
NeurIPS
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Ivan Ustyuzhaninov, Santiago A. Cadena, Emmanouil Froudarakis, Paul G. Fahey, Edgar Y. Walker, Erick Cobos, Jacob Reimer, Fabian H. Sinz, Andreas S. Tolias, Matthias Bethge, Alexander S. Ecker
Rotation-invariant clustering of functional cell types in primary visual cortex
ICLR (accepted for talk)
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2019
Santiago A. Cadena, Fabian H. Sinz, Taliah Muhammad, Emmanouil Froudarakis, Erick Cobos, Edgar Y. Walker, Jake Reimer, Matthias Bethge, Andreas Tolias, Alexander S. Ecker
How well do deep neural networks trained on object recognition characterize the mouse visual system?
NeurIPS workshop on Real Neurons & Hidden Units (accepted for talk)
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Alexander S. Ecker, Fabian H. Sinz, Emmanouil Froudarakis, Paul G. Fahey, Santiago A. Cadena, Edgar Y. Walker, Erick Cobos, Jacob Reimer, Andreas S. Tolias, Matthias Bethge
A rotation-equivariant convolutional neural network model of primary visual cortex
ICLR 2019
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S. A. Cadena, G. H. Denfield, E. Y. Walker, L. A. Gatys, A. S. Tolias, M. Bethge, and A. S. Ecker
Deep convolutional models improve predictions of macaque V1 responses to natural images
PLoS Computational Biology
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2018
Fabian H. Sinz, Alexander S. Ecker, Paul G. Fahey, Edgar Y. Walker, Erick Cobos, Emmanouil Froudarakis, Dimitri Yatsenko, Xaq Pitkow, Jacob Reimer, Andreas S. Tolias
Stimulus domain transfer in recurrent models for large scale cortical population prediction on video
NeurIPS
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